Knowledge-guided, fully automated image analytic procedures will be applied, and further developed for the extraction of diagnostic and prognostic information from histopathologic sections. It is proposed to develop knowledge files for the grading of solar lesions, for the analysis of prostatic intraepithelial neoplastic lesions (PIN), for benign proliferative epithelial lesions of the breast, and for kidney tumors. Quantitative progression indices will be derived from histometric measurements. These may serve to identify patients at high risk to develop infiltrating disease, to measure rate of lesion progression, and to allow a numeric assessment of the efficacy of chemopreventive intervention. Knowledge files are under development for a quantitative measurement of the vascularization around PIN lesions. For nuclei, lesions and patients, novel methodology is proposed to characterize these entities by identification, rather than by mere classification. This will allow a significantly more precise characterization of the nuceli in a lesion and of the state of lesion progression. The identification methods will be integrated into the current diagnostic decision support system, and be given capabilities to handle missing data, contradictory evidence, atypical diagnostic clue expression. This capability relies on automated reasoning will be developed, and the methodology will be adapted for used in histopathologic diagnosis.